Abstract: System, methods, and other embodiments described herein relate to selectively intervening in manual control of a vehicle by a driver. In one embodiment, a method includes predicting a future state of the vehicle according to at least a current state and a control input. The current state defines at least one attribute of a current trajectory of the vehicle, and the control input defines at least one driver input for controlling the vehicle. The method includes comparing the future state with a state constraint indicating a range within which a target path of the vehicle is acceptable. The target path defines a subsequent trajectory for the vehicle. The method includes selectively modifying the target path according to whether the future state violates the state constraint. The method includes controlling the vehicle according to the target path.

Abstract: A system including a controller configured to, in each sampling period, minimize a distance of the autonomous vehicle from a target path by solving a constrained control problem, input sensor values and estimators that are calculated based on the sensor values and dynamic models and record the sensor values and the estimators in a memory of the controller, incorporate the sensor values and the estimators into conditions for minimizing the distance of the autonomous vehicle from the target path associated with the constrained control problem, map the conditions for minimizing the distance of the autonomous vehicle from the target path to a non-smooth system using Fischer-Burmeister function, smooth the non-smooth system and apply Newton method iterations to the smoothed system in order to converge on a solution, and issue commands including a steering command that control actuators of the autonomous vehicle based on the solution.

Abstract: System, methods, and other embodiments described herein relate to selectively intervening in manual control of a vehicle by a driver. In one embodiment, a method includes predicting a future state of the vehicle according to at least a current state and a control input. The current state defines at least one attribute of a current trajectory of the vehicle, and the control input defines at least one driver input for controlling the vehicle. The method includes comparing the future state with a state constraint indicating a range within which a target path of the vehicle is acceptable. The target path defines a subsequent trajectory for the vehicle. The method includes selectively modifying the target path according to whether the future state violates the state constraint. The method includes controlling the vehicle according to the target path.

Abstract: System for controlling an autonomous vehicle having. The system includes a controller. In each sampling period, the controller inputs sensor values and estimators that are calculated based on the sensor values and dynamic models and records the sensor values and the estimators in a memory of the controller. The controller incorporates the sensor values and the estimators into conditions for optimality associated with a constrained optimal control problem, maps the conditions for optimality to a non-smooth system using Fischer-Burmeister function, performs Newton method iterations on a smoothed system approximating the non-smooth system in order to converge on a solution, and issues commands that control actuators during vehicle operation.